Neural Network Documentation
Learn how ForSure's neural network learns from your coding style to create personalized project structures
Introduction
ForSure's neural network is an advanced AI system designed to learn from your coding style and project structures. As you use ForSure to create and manage projects, the neural network analyzes your patterns, preferences, and conventions to deliver increasingly personalized results.
This adaptive learning system helps maintain consistency across projects while reducing the cognitive load of decision-making around project organization. The more you use ForSure, the better it understands your style.
Beta Feature
The neural network feature is currently in beta. While it's fully functional, we're continuously improving its capabilities and performance based on user feedback.
How It Works
ForSure's neural network operates through a multi-stage process that combines analysis, learning, and application to deliver personalized project structures.
When you first use ForSure, the neural network analyzes your existing projects (if you choose to share them) or starts with a neutral baseline. It examines:
- File and directory naming conventions
- Project structure organization
- Code formatting preferences
- Common patterns in your projects
The neural network identifies recurring patterns in your work using advanced machine learning algorithms:
- Consistent naming patterns (camelCase, PascalCase, etc.)
- Directory organization strategies
- File grouping preferences
- Common project structures you favor
As you create more projects with ForSure, the neural network refines its understanding:
- Updates its model with each new project you create
- Learns from your modifications to suggested structures
- Adapts to evolving preferences over time
- Builds a comprehensive style profile unique to you
When generating new project structures, the neural network applies your style profile:
- Creates structures that match your organizational preferences
- Applies your naming conventions consistently
- Suggests appropriate file groupings based on your patterns
- Balances your preferences with project-specific requirements
Technical Architecture
ForSure's neural network is built on a sophisticated architecture designed specifically for code and project structure analysis.
Core Components
A transformer-based architecture optimized for code structure analysis, with specialized attention mechanisms for hierarchical data.
Converts project structures into vector representations that capture organizational patterns and naming conventions.
Identifies recurring patterns in project structures and extracts rules that define your personal style.
Learning Process
// Simplified representation of the neural network learning process
// 1. Encode project structure
function encodeProjectStructure(project) {
const structureVector = styleEncoder.encode(project.fileStructure);
const namingVector = styleEncoder.encode(project.namingConventions);
const organizationVector = styleEncoder.encode(project.organization);
return {
structureVector,
namingVector,
organizationVector
};
}
// 2. Extract patterns
function extractPatterns(encodedProjects) {
const patterns = patternExtractor.analyze(encodedProjects);
return patterns;
}
// 3. Update neural model
function updateModel(patterns, userFeedback) {
const learningRate = calculateAdaptiveRate(userHistory);
neuralModel.update(patterns, userFeedback, learningRate);
return neuralModel.getUpdatedWeights();
}
// 4. Generate personalized structure
function generateStructure(projectRequirements) {
const userStyleProfile = neuralModel.getUserProfile();
const baseStructure = templateEngine.getBaseStructure(projectRequirements);
return structureGenerator.apply(baseStructure, userStyleProfile);
}Data Processing Pipeline
Input
Project files, structures, and user feedback
Processing
Pattern extraction and style analysis
Model Update
Neural network training and adaptation
Output
Personalized project structures
Using the Neural Network
ForSure's neural network is designed to work seamlessly in the background, but you can also configure and interact with it directly.
Getting Started
The neural network is enabled by default for all users. As you create projects with ForSure, it will automatically begin learning your style preferences.
First-time users
Initial Setup
To accelerate the learning process, you can provide existing projects for analysis:
# Analyze existing projects to train the neural network
forsure analyze --path ./my-projects --recursive
# Check the neural network's learning status
forsure ai status
# Generate a new project with neural network assistance
forsure new my-new-project --ai-enhancedConfiguration Options
You can configure the neural network's behavior through the ForSure CLI or web application settings.
# Enable or disable the neural network
forsure config set ai.enabled true
# Set the learning rate (0.1-1.0, higher values adapt faster)
forsure config set ai.learningRate 0.5
# Configure which aspects to analyze
forsure config set ai.analyze.naming true
forsure config set ai.analyze.structure true
forsure config set ai.analyze.formatting true
# Reset the neural network to start fresh
forsure ai resetViewing Neural Network Insights
ForSure provides tools to view insights about what the neural network has learned about your style.
# View a summary of your style profile
forsure ai profile
# Output:
# Style Profile Summary
# --------------------
# Naming Convention: Primarily camelCase (87% confidence)
# Directory Structure: Feature-based organization (92% confidence)
# File Grouping: Co-located tests and components (78% confidence)
# Common Patterns:
# - Components in separate directories
# - Utility functions grouped by domain
# - Configuration files at root level
# View detailed analysis of a specific aspect
forsure ai profile --aspect naming
# Export your style profile (can be imported on another machine)
forsure ai profile export --output my-style-profile.jsonWeb Dashboard
The web application includes a neural network dashboard that provides visualizations of your style profile, learning progress, and personalization insights. Access it at Settings → AI & Neural Network → View Dashboard.
Best Practices
Follow these recommendations to get the most out of ForSure's neural network capabilities.
- Analyze at least 3-5 existing projects to establish a baseline
- Include diverse project types for more comprehensive learning
- Manually review and correct initial suggestions to accelerate learning
- Provide feedback on generated structures to improve accuracy
- Periodically check the neural network insights to understand its learning
- Use the `--ai-enhanced` flag consistently for best results
- Create a shared team profile for consistent project structures
- Export and share style profiles with new team members
- Use project templates alongside neural network for standardization
- Adjust learning rate based on how quickly you want adaptation
- Focus analysis on aspects most important to your workflow
- Reset the neural network if your style preferences change significantly
Pro Tip: Balancing AI and Manual Control
For optimal results, use the neural network as a starting point but maintain manual control over critical aspects of your project structure. This hybrid approach ensures you get the benefits of AI assistance while preserving your specific requirements for each project.
Do: Review and adjust AI-generated structures before finalizing
Don't: Rely solely on AI without reviewing the output
Do: Provide feedback to improve future suggestions
Don't: Ignore the learning process by constantly overriding without feedback
Troubleshooting
If you encounter issues with the neural network feature, here are some common problems and their solutions.
Neural Network Not Learning My Style
If the neural network doesn't seem to be adapting to your style preferences:
- Verify that the neural network is enabled in your settings
- Check that you've created enough projects (at least 3-5) for effective learning
- Try increasing the learning rate in your configuration
- Run `forsure analyze` on your existing projects to provide more training data
- Ensure you're using the `--ai-enhanced` flag when generating new projects to apply personalization
# Verify neural network status
forsure ai status
# Analyze existing projects to improve learning
forsure analyze --path ./my-projects --recursive
# Increase learning rate for faster adaptation
forsure config set ai.learningRate 0.8Incorrect Style Predictions
If the neural network is generating structures that don't match your preferences:
- View your style profile to understand what the AI has learned
- Provide explicit feedback on generated structures
- Consider resetting the neural network if your preferences have changed significantly
- Focus the analysis on specific aspects that are most important to you
# View your current style profile
forsure ai profile
# Provide feedback on a generated structure
forsure feedback --project my-project --rating 3 --comments "Directory structure is good, but naming needs improvement"
# Reset the neural network to start fresh
forsure ai resetPerformance Issues
If you experience slowdowns or performance issues with the neural network:
- Reduce the scope of analysis by focusing on fewer aspects
- Use local storage instead of cloud storage for the neural network model
- Update to the latest version of ForSure for performance improvements
- Consider disabling the neural network for very large projects if necessary
# Focus analysis on specific aspects only
forsure config set ai.analyze.naming true
forsure config set ai.analyze.structure true
forsure config set ai.analyze.formatting false
forsure config set ai.analyze.comments false
# Use local storage only
forsure config set ai.storage.cloud false
# Temporarily disable for a specific project
forsure new large-project --no-aiFrequently Asked Questions
Is my code or project data shared with others?
No. Your code and project data remain private. The neural network runs locally on your machine by default. You can optionally enable anonymous data sharing to help improve the system, but this is disabled by default and only shares non-identifying pattern information, never your actual code.
How long does it take for the neural network to learn my style?
The neural network begins learning immediately, but you'll typically see noticeable personalization after creating 3-5 projects. The more projects you create, the more refined the personalization becomes. You can accelerate this process by analyzing existing projects.
Can I have multiple style profiles for different types of projects?
Yes. You can create and switch between multiple style profiles. This is useful if you work on different types of projects (e.g., frontend vs. backend) that require different organizational approaches.
# Create a new profile
forsure ai profile create frontend-profile
# Switch to a different profile
forsure ai profile use backend-profile
# List available profiles
forsure ai profile listCan I export my style profile to use on another machine?
Yes. You can export your style profile and import it on another machine or share it with team members.
# Export your style profile
forsure ai profile export --output my-style.json
# Import a style profile
forsure ai profile import --input my-style.jsonDoes the neural network work with all programming languages?
Yes. The neural network focuses on project structure and naming conventions, which are language-agnostic. It works with any programming language or framework that ForSure supports.
What if I want to override the neural network's suggestions?
You always have full control. The neural network provides suggestions, but you can modify or override them at any time. You can also provide feedback to help the system learn from your corrections.
Advanced Topics
Team Collaboration
ForSure's neural network can be used in team environments to maintain consistency across projects while still respecting individual preferences.
Teams can create shared style profiles that combine the preferences of all team members or enforce team standards.
# Create a team profile
forsure team create dev-team
# Add members to the team
forsure team add-member dev-team --user alice@example.com
forsure team add-member dev-team --user bob@example.com
# Create a shared style profile for the team
forsure ai profile create --team dev-team
# Analyze team projects to build the shared profile
forsure analyze --team dev-team --path ./team-projects
# Use the team profile for a new project
forsure new project-name --team dev-teamCustom Neural Network Training
Advanced users can customize how the neural network learns and what aspects of your style it focuses on.
You can create a custom training configuration to focus on specific aspects of your coding style.
{
"training": {
"focus": {
"naming": 0.8,
"structure": 1.0,
"formatting": 0.4,
"comments": 0.2
},
"weights": {
"consistency": 0.7,
"innovation": 0.3
},
"adaptationRate": {
"initial": 0.5,
"decay": 0.05,
"minimum": 0.1
}
}
}Apply the custom configuration:
forsure ai configure --config ai-training-config.jsonIntegration with CI/CD Pipelines
You can integrate ForSure's neural network capabilities into your CI/CD pipelines to ensure consistent project structures across your organization.
Example of integrating ForSure with GitHub Actions:
name: ForSure Project Structure Check
on:
pull_request:
branches: [ main ]
jobs:
structure-check:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Install ForSure
run: npm install -g forsure-cli
- name: Import team style profile
run: |
echo "${{ secrets.FORSURE_TEAM_PROFILE }}" > team-profile.json
forsure ai profile import --input team-profile.json
- name: Analyze project structure
run: |
forsure analyze --path ./ --output analysis.json
- name: Check compliance with team standards
run: |
forsure validate --profile team --input analysis.json --threshold 0.8API Reference
ForSure provides a comprehensive API for programmatic interaction with the neural network capabilities.
const { ForSure, NeuralNetwork } = require('forsure');
// Initialize ForSure with neural network enabled
const forsure = new ForSure({
neuralNetwork: {
enabled: true,
learningRate: 0.5
}
});
// Analyze existing projects
async function analyzeProjects() {
const results = await forsure.neuralNetwork.analyze({
path: './my-projects',
recursive: true
});
console.log('Analysis complete:', results);
}
// Generate a project with neural network assistance
async function generateProject() {
const project = await forsure.generate({
name: 'my-new-project',
template: 'react-app',
aiEnhanced: true
});
console.log('Project generated at:', project.path);
}
// Get the current style profile
async function getStyleProfile() {
const profile = await forsure.neuralNetwork.getProfile();
console.log('Style profile:', profile);
}
// Provide feedback to improve learning
async function provideFeedback() {
await forsure.neuralNetwork.feedback({
projectPath: './my-project',
rating: 4,
comments: 'Good structure, but naming could be improved'
});
}
// Export the neural network model
async function exportModel() {
const modelData = await forsure.neuralNetwork.export();
fs.writeFileSync('my-model.json', JSON.stringify(modelData));
}
// Import a neural network model
async function importModel() {
const modelData = JSON.parse(fs.readFileSync('my-model.json'));
await forsure.neuralNetwork.import(modelData);
}Next Steps
Now that you understand ForSure's neural network capabilities, here are some next steps to explore: